Machine Learning Engineer

• Responsible for developing and operationalizing AI solutions, with a focus on scalability, governance and the industrialization of models.
• Act as the technical reference for AI and MLOps, leading the construction, deployment and evolution of models in production.
• Define the architecture and standards for the AI and MLOps pipeline.
• Develop and operationalize models (predictive and generative).
• Design ML/LLMOps pipelines (training, deployment and monitoring).
• Implement CI/CD, version control and model monitoring.
• Ensure governance, traceability and quality.
• Translate business requirements into technical solutions.

Technical Lead - AI, MLOps

AI Engineer

• Lead the adoption and evolution of agentic development tools (with Claude Code as the primary tool) within multidisciplinary squads.
• Define and disseminate AI-assisted development methodologies — based on Spec-Driven Development (Spec-Kit, BMAD) — adapting the lifecycle to each product’s context.
• Design and implement agentic processes across the engineering workflow: AI-assisted code review, generation and maintenance of tests, assisted refactoring, living documentation, and PR automation.
• Mentor peers with support, raising the team’s maturity in AI-assisted engineering.
• Support the squad’s backlog organization, prioritizing technical debt, architectural risks, and AI automation opportunities.

Mid level/Senior, AI Engineer

• Você vai construir a plataforma de agentes de IA da Guanabara e o ecossistema que distribui IA generativa governada à companhia.
• Orquestração de agentes — construir agentes com LangGraph no padrão Supervisor/ReAct, com memória de curto/longo prazo e estado durável (PostgresSaver).
• Camada semântica e RAG — evoluir a camada semântica determinística (Cube.js) e a recuperação semântica com Qdrant (busca vetorial + re-ranking) que ancoram as respostas em dado real.
• Skills, subagentes e MCP — criar e versionar skills (SKILL.md), subagentes e ferramentas MCP (FastMCP) para o Claude Code, em fluxo spec-driven, conectando agentes a sistemas corporativos.
• Verificação e avaliação — manter o verificador (LLM-as-judge) e suítes de eval (offline/online) com amostragem de traces para pegar drift de qualidade.
• Plataforma de modelos — operar o gateway LiteLLM (Virtual Keys, budget, RBAC, roteamento multi-provider) otimizando custo e latência.
• Adaptação e serving de modelos — quando RAG e prompting não bastam, fazer fine-tuning supervisionado e PEFT (LoRA/QLoRA), quantização e destilação; servir modelos abertos/privados com vLLM e roteamento SLM↔LLM.
• Governança e segurança — aplicar guardrails de PII (Presidio), defesa contra prompt injection e observabilidade ponta a ponta (Langfuse).

Senior AI Engineer – Agentes, Plataforma & RAG

• You are the one who connects the prototype to production.
• You will design and build the ML engineering for the pricing engine — the inference serving, training pipelines, and feature engineering — so that complex models run in real time with low latency on Ray.
• Focus on modeling and ML code; the platform and runtime are owned by the MLOps/Platform team, with whom you will work closely.
• Inference serving — design the pipeline of chained models on Ray Serve — model composition, low latency, and update strategies.
• Distributed training — build training pipelines (Ray Train/Data), HPO (Ray Tune), and per-tenant trained models with resilient checkpointing.
• Feature engineering — define and materialize features in the feature store (Feast/Redis), ensuring consistency between training and production.
• Optimization and RL — implement and optimize the optimization components (linear programming) and offline RL of the pricing pipeline.
• Model quality — monitor drift from a modeling perspective, validate versions, and produce explainability (SHAP) — in partnership with MLOps.
• Technical leadership — serve as a reference, mentor, and define with the team what is feasible and scalable.
• You perform handoffs with data scientists, receive data from data engineers, and deliver to the MLOps/Platform team for deployment and operation.

Senior Machine Learning Engineer

• Ajudar grandes empresas a transformar o potencial da AI em impacto real nos negócios com AI Deployment, execução AI-native e tech-integrated business solutions.
• Colaborar para construir soluções com impacto real.
• Manter controle de qualidade e operar em produção com segurança.
• Estruturar especificações que alimentam a IA.

Senior AI Developer

• Desenvolver protótipos e MVPs com IA generativa, automações e integrações.
• Criar fluxos automatizados utilizando n8n e integrações via APIs.
• Atuar com ferramentas como Claude, Claude Code, Lovable, OpenAI e similares.
• Desenvolver ou evoluir soluções usando duas ou mais linguagens (Node.js, React, PHP e Python).
• Integrar sistemas internos, bancos de dados, aplicações web e ferramentas de IA.
• Apoiar desenho técnico de agentes, copilots, assistentes e fluxos inteligentes.
• Avaliar viabilidade técnica, esforço, riscos e arquitetura das soluções.
• Apoiar boas práticas de segurança, logs, autenticação e uso seguro de dados.
• Documentar soluções e apoiar a evolução de MVPs para produção.
• Atuar consultivamente no mapeamento de processos As-is e proposição do To-Be.

Senior AI Engineer

• Projetar, implementar e escalar soluções baseadas em Modelos de Linguagem de Larga Escala (LLMs);
• Construção de APIs: Desenvolver e manter microsserviços robustos em Python e Java;
• Orquestração e Integração: Configurar e gerenciar o fluxo de dados entre o backend e API Gateways;
• Ciclo de Vida de IA (AIOps): Implementar práticas de AIOps para monitoramento de modelos em produção;
• Desenvolvimento de Agentes: Projetar e implementar agentes de IA autônomos;
• Otimização de LLMs: Aplicar técnicas de Prompt Engineering e fine-tuning;
• Arquitetura de Dados: Garantir que as integrações de backend suportem o processamento de dados em tempo real.

• Develop generative AI applications using frameworks such as Langchain, applying advanced techniques like RAG and Prompt Engineering.
• Collaborate with research and development teams to integrate GenAI solutions into products and services.
• Analyze large datasets to optimize AI models.
• Use Function Calling techniques to enhance the functionality and integration of AI models.

Senior Machine Learning/GenAI Analyst

• Take ownership of the ML API that serves NBA recommendations, partnering with the data engineer who's been building it, and harden it for low-latency production traffic.
• Ship your first agent tool contract end-to-end: schema design, handler implementation, structured-error contract, unit tests, deployed via HAL's runtime.
• Set up the eval foundation for our agents: golden transcripts, rubric-based judges, regression suites that run on every prompt or model change.
• Build a working relationship with HAL and become the data team's go-to on agent infrastructure decisions.
• Be the primary owner (with data engineer support) of the ML API and the agent tool layer that wraps NBA and our ML models.
• Have shipped at least one production-grade agent (customer-facing or partner-facing) with prompt versioning, evals, observability, and multi-tenant gating in place.
• Define the data team's playbook for shipping a new ML model as an LLM-callable tool, end-to-end.
• Mentor the data engineers on ML/AI patterns so they can confidently support and extend the systems you own.
• Operate as the technical lead within the data team for NBA production AI at Clutch — the person other teams come to when they want to understand how NBA ships ML and agents responsibly.
• Have measurably improved agent cost and latency (target: 30%+ reduction on P95 latency or per-conversation cost on at least one agent).
• Be shaping the data team's roadmap for the next generation of ML and AI products, in partnership with the PM and data scientist.
• Help us decide what to hire next as the team scales.

Senior ML Engineer

• Design, implement, and optimize the data infrastructure that supports AI and machine learning (ML) solutions
• Ensure data availability, quality, and security
• Ingest and transform data for the data lake and Feature Store
• Develop and support the operationalization of AI and ML models
• Conduct complex analyses and monitor data quality

Senior AI Engineer, ADK GCP

• Provide technical leadership to more junior members of the Applied AI Engineering team;
• Identify opportunities to apply AI to drive business impact;
• Perform exploratory data analysis and data visualization to understand business needs and inform LLM applications;
• Develop patterns and practices to improve the scalability of LLM applications;
• Optimize the Applied AI Engineering development stack (CI/CD, evaluation of libraries, etc.);
• Develop AI applications using vector databases, LLMs, single-agent and multi-agent architectures;
• Build validation pipelines for LLM applications, including datasets and using LLMs as a judge;
• Develop small back-end and front-end components to validate proofs of concept for LLM applications;
• Actively research technical blogs, academic papers and video conferences to generate improvement and optimization hypotheses for LLM applications;

• Develop and maintain model training, validation and inference pipelines.
• Design feature and data pipelines that support machine learning applications in production.
• Convert models developed in exploratory environments into reproducible, scalable pipelines.
• Integrate machine learning solutions into the organization’s data ecosystem.
• Collaborate with engineering, DevOps and DataOps teams to evolve the platform and the organization’s machine learning practices.
• Work closely with data scientists to operationalize analytical models.
• Support the transition of models from exploratory environments to production pipelines.
• Build pipelines that enable recurring training, updating and model evolution.
• Ensure models can run at scale and meet performance requirements for operational use.
• Prepare, structure and transform the data required for model training and execution.
• Develop data pipelines and feature engineering focused on machine learning applications.
• Work with data engineering to ensure the availability and quality of the data used by models.
• Implement monitoring mechanisms for model performance, stability and behavior in production.
• Support the identification of model degradation and the need for retraining.
• Contribute to the continuous improvement of machine learning solutions in production.
• Ensure software development best practices are applied to machine learning solutions.
• Use code versioning, code review and engineering practices for developing ML pipelines.
• Contribute to the standardization and evolution of the area’s machine learning engineering practices.

• We are seeking a Machine Learning Engineer (MLOps) to join our data team to help combat cybercrime and prevent money laundering.
• You will be responsible for ensuring our machine learning models are deployed, monitored, and maintained in production with operational excellence, scalability, and reliability.
• Develop and maintain automated ML pipelines (CI/CD) for model training, validation, and deployment
• Adapt data transformation pipelines for machine learning model inference
• Ensure scalability and availability of ML applications in production environments
• Collaborate with Data Scientists to build robust, scalable pipelines
• Deploy continuous monitoring systems for model performance in production
• Identify and mitigate performance degradation, data drift, and concept drift
• Establish alerts and dashboards to track critical metrics (Grafana or similar)
• Implement automated retraining strategies and model versioning
• Update models with new data while maintaining traceability and governance
• Provide infrastructure for explainability techniques (XAI), ensuring transparency and regulatory compliance
• Explore and evaluate new MLOps technologies, frameworks, and tools

AI Research Scientist

• Research audio data quality for machine learning
• Investigate how audio quality, signal properties, dataset composition, and localized acoustic issues affect downstream model training, evaluation, and deployment.
• Develop new metrics, benchmarks, diagnostics, and evaluation frameworks for measuring audio data quality in ways that are predictive of ML model performance.
• Analyze and summarize Protege’s audio catalog and maintain clear, up-to-date quality scorecards and metrics for key speech datasets.
• Develop methods to measure true acoustic properties directly from the waveform, including effective bandwidth, spectral energy distribution, high-frequency roll-off, noise, clipping, reverberation, distortion, and codec artifacts.
• Build workflows that evaluate diarized or segmented speech regions, surfacing localized degradation that file-level averages may miss.
• Design and run targeted evaluations connecting audio quality issues to downstream model behavior, including ASR performance, speaker embedding stability, learned speech representations, and synthesis quality.
• Translate research findings into reproducible filtering rules, quality gates, and dataset selection strategies that improve dataset consistency across training runs.

Machine Learning Researcher – Audio

• Work on development, automation and artificial intelligence, using automation and AI tools to achieve operational efficiency gains, reduce manual tasks, and continuously improve information flows.
• Create process automations using tools such as n8n, UiPath, Orchestrator or similar.
• Integrate different data sources (APIs, relational databases, files, internal and external systems).
• Apply Data Science and AI techniques to optimize processes and generate insights.
• Support the implementation of applied AI solutions (classification, prediction, cognitive automation).
• Ensure the quality, consistency and traceability of the data used in automated processes.
• Document flows, business rules and automations developed.
• Collaborate with business, engineering and analytics teams to understand requirements.
• Participate in POCs with vendors and new automation and AI technologies.

• Design and implement multi-agent AI architectures using AWS Bedrock
• Develop agent orchestration logic and collaborative agent workflows
• Configure and manage AWS Bedrock Agents, Knowledge Bases, and Guardrails
• Build Retrieval-Augmented Generation (RAG) pipelines using vector databases and embeddings
• Implement tool integrations using Model Context Protocol (MCP) and API-based services
• Optimize LLM behavior through prompt engineering, tuning, and context management
• Develop observability and monitoring strategies for AI workflows using CloudWatch and X-Ray
• Build scalable event-driven architectures and resilient integration patterns
• Design error handling, retry strategies, and graceful degradation mechanisms
• Collaborate with engineering, product, and architecture teams to deliver production-grade AI solutions
• Support infrastructure automation and deployment pipelines using IaC and CI/CD practices
• Ensure governance, security, auditability, and compliance standards across AI systems

Master AI / Machine Learning Specialist, AWS

• Design and build datasets, tasks, and environments for benchmarking agentic systems and multi-step model behavior.
• Translate real-world workflows into structured tasks, interaction traces, trajectories, stateful environments, and verifiable outcomes that can be used to evaluate advanced AI systems.
• Develop frameworks that assess diversity, realism, coverage, fidelity, informativeness, and downstream usefulness of datasets for agentic systems.
• Build quality scorecards and evaluation methods that make dataset strengths, weaknesses, and failure modes legible across teams.
• Evaluate planning, tool use, robustness, recovery from failure, task completion, and generalization behavior in RL-style or agentic environments.
• Connect model failures back to concrete dataset, environment, or task-design gaps and recommend improvements grounded in empirical evidence.
• Contribute to tools and systems that automate dataset validation, environment generation, rollout analysis, benchmark construction, and evaluation workflows.
• Improve internal infrastructure for reproducible experimentation, benchmark management, and evaluation quality.
• Collaborate closely with research and engineering teams to identify data bottlenecks, improve evaluation methodology, and shape internal best practices around task-grounded AI training data.
• Represent DataLab’s perspective in cross-functional discussions around dataset quality, benchmark design, and frontier agentic-system evaluation.

Machine Learning Researcher – RL and Agentic Systems

• Be responsible for the squad's technical delivery, ensuring quality, predictability and adherence to architectural standards
• Lead technical refinement with the product team, translating business needs into viable, well-sized solutions
• Support the squad backlog organization, prioritizing technical debt, architectural risks and AI automation opportunities
• Lead the adoption and evolution of agent-based development tools (Claude Code in particular) in multidisciplinary squads
• Define and disseminate AI-assisted development methods — using Spec-Driven Development (Spec-Kit, BMAD) as a foundation — adapting the lifecycle to each product context
• Design and implement agent-driven processes across the engineering workflow: AI-assisted code review, test generation and maintenance, assisted refactoring, living documentation, PR automation
• Act as a systems architect with an AI perspective: evaluate trade-offs, propose integration patterns between agents and services, ensure consistency across iOS, Android and BFF
• Mentor engineers, raising the team's maturity in AI-assisted engineering

AI Engineer – Software Architect

• Operate the SDLC natively with AI
• Maintain quality control and operate securely in production
• Structure specifications that feed the AI
• Interact with diverse tools and environments

AI Developer – PL/SR

• Operate the Software Development Life Cycle (SDLC) natively with AI
• Consistent use of automations with agents that perform multi-step tasks
• Structure specifications that feed the AI
• Maintain quality control and operate securely in production

See all jobs on JobTailor

Technical Lead - AI, MLOps

Mid level/Senior, AI Engineer

Senior AI Engineer – Agentes, Plataforma & RAG

Senior Machine Learning Engineer

Senior AI Developer

Senior AI Engineer

Senior AI Engineer

Senior Machine Learning/GenAI Analyst

Senior ML Engineer

Senior AI Engineer, ADK GCP

Senior AI Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Researcher – Audio

AI Engineer

Master AI / Machine Learning Specialist, AWS

Machine Learning Researcher – RL and Agentic Systems

AI Engineer – Software Architect

AI Developer – PL/SR

AI Developer – Mid/Senior